Tomosynthesis obtains volume image data by directing x-ray radiation through a patient from a range of angles. At each angle, corresponding 2-dimensional (2-D) projection image data is acquired. Volume reconstruction techniques then generate 3-dimensional (3-D) image voxel data using information from the set of projection image data.
For some types of volume image content, there can be a very wide range of bone and tissue densities, requiring a correspondingly wide contrast range. Even though a wide range is needed, contrast for tomosynthesis image slices can be disappointing. For chest images, for example, it can be difficult to distinguish features such as lung tissue textures without applying some type of image enhancement. Volume image content, displayed as image slices, often lacks sufficient contrast needed for accurate analysis and may not provide diagnostically useful information.
Because of the relatively anisotropic resolution that is provided by tomosynthesis imaging, image enhancement techniques that have been used with other types of radiographic imaging can fall short of what is needed for contrast improvement. Among problems encountered with conventional image processing approaches are conflicting requirements for localized contrast within a small region of interest within an image slice and global contrast across the image slice. Thus, it can be appreciated that there is a need for image processing solutions that improve local contrast without compromising overall contrast for the complete image slice.